Bias and Fairness in Computer Vision Applications of the Criminal Justice System
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Abstract (arXiv)
Discriminatory practices involving AI-driven police work have been the subject of much controversies in the past few years, with algorithms such as COMPAS, PredPol and ShotSpotter being accused of unfairly impacting minority groups. At the same time, the issues of fairness in machine learning, and in particular in computer vision, have been the subject of a growing number of academic works. In this paper, we examine how these area intersect. We provide information on how these practices have come to exist and the difficulties in alleviating them. We then examine three applications currently in development to understand what risks they pose to fairness and how those risks can be mitigated.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 5 Aug 2022.
Builds on
10 of 39 references.
References · 10
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
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- 2019Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchycited by 156
- 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
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- 2016PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptorscited by 16
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.